About me
I’m a first-year PhD at the Department of Materials and Engineering, Cornell University (CU). I worked on scanning transmission electron microscopy (STEM) with Prof. Wu Zhou from STEM Group at the University of Chinese Academy of Sciences (UCAS) in my undergraduate. To pursue my goal, I also worked on universal materials discovery as a visiting student in Prof. Gerbrand Ceder’s group at UC Berkeley. Aligned with my goal of integrating scientific research with machine learning (ML), my interests lie in the fields (including but not limited to):
(1) ML-driven computational materials science: accelerate materials discovery with machine learning tools, including ML interatomic potential and generative model.
(2) ML-driven data analysis in electron microscopy: develop machine learning techniques to improve spectral and imaging quality, track material defects, and enhance chemical characterization accuracy.
(3) Data-centric scientific discovery from electron microscopy: explore structural characteristics and relationship between structure and properties within complex materials, like amorphous, via data-centric methods.
During my junior year, I attended MIT as an exchange student where I had the privilege of working with Prof. Rodrigo Freitas on crystal structure identification in atomistic simulation.
I also lead a group of enthusiastic undergraduates dedicated to implementing advanced machine learning tools in the analysis of electron microscopic (EM) data. Our ultimate goal is to build a high-throughput and automatic digital analysis EM platform, eliminating possible barriers to SOTA methods for microscopists.
Plus, I initiated an academic discussion community at UCAS for students sharing common interests in integrating machine learning into science. Currently, the community members cover students from UCAS, Peking University, Tsinghua University, UIUC, NYU, etc.
You can find my CV here: Xinzhe Dai’s Curriculum Vitae.